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Artificial Intelligence · Major Project
Road Accident Detection & Alert with YOLOv8
Detects crashes in CCTV footage and pushes an alert with a snapshot
3-4 weeks build
Overview
A YOLOv8 model fine-tuned on a public road-accident image dataset flags collision scenes in CCTV video, and a confirmation step requires the detection to persist across several frames before anything fires. Confirmed incidents are logged with a snapshot and camera location, and an alert goes out by SMS or email so a control room can dispatch help.
What makes it stand up
- 01YOLOv8 fine-tuned on accident and normal traffic scenes
- 02Multi-frame confirmation to suppress one-off false positives
- 03Snapshot, timestamp and camera location in every alert
- 04SMS and email alerts through official provider APIs
- 05Confusion matrix and per-class F1 in the report
Module breakdown
- 01
Dataset
Public accident images cleaned, split and augmented.
- 02
Detector
YOLOv8 training, validation and export.
- 03
Stream processor
Frame sampling and persistence-based confirmation.
- 04
Alert service
FastAPI endpoint sending SMS and email with evidence.
- 05
Incident log
Searchable list of past alerts with snapshots.
After this, you will be able to
- Explain how temporal confirmation reduces false alarms
- Defend your dataset split and evaluation metrics
- Demo an alert arriving from a recorded crash clip
Technology stack
- Python
- YOLOv8
- OpenCV
- FastAPI
- Twilio
- Level
- Major Project
- Domain
- Artificial Intelligence
- Build time
- 3-4 weeks
- Difficulty
You receive
- Complete, commented source code
- 60-90 page project report (IEEE format)
- Editable presentation deck
- Architecture, ER and UML diagrams
- Local setup and deployment guide
- Sample dataset or seed data
- Viva question bank with answers
- One 45-minute walkthrough call
- 30 days of doubt-clearing support